122 lines
4.7 KiB
Python
122 lines
4.7 KiB
Python
import json
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import os
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import pandas as pd
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from pathlib import Path
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from tabulate import tabulate
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# 定义结果目录
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data_dir = Path('./result')
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# 指定要分析的文件
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target_file = data_dir / 'backtest_trades.json'
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print(f"分析文件: {target_file}")
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# 读取文件
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try:
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with open(target_file, 'r', encoding='utf-8') as f:
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trades = json.load(f)
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print(f"文件加载成功，包含 {len(trades)} 笔交易")
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except Exception as e:
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print(f"文件加载失败: {e}")
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exit(1)
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# 分析加仓情况
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results = []
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trades_with_adjustments = 0
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for i, trade in enumerate(trades):
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# 每1000笔交易显示进度
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if (i + 1) % 1000 == 0:
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print(f"已分析 {i + 1} 笔交易")
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pair = trade.get('pair', 'Unknown')
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open_date = trade.get('open_date', 'Unknown')[:10] # 只取日期部分
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# 获取entries信息
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entries = trade.get('entries', [])
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# 筛选出包含加仓的交易
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if len(entries) > 1:
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trades_with_adjustments += 1
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base_price = entries[0]['price']
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# 计算每次加仓相比前一次的降幅
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prev_price = base_price
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for j in range(1, len(entries)):
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current_entry = entries[j]
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current_price = current_entry['price']
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if prev_price > 0 and current_price > 0:
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# 计算降幅百分比
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price_drop_percent = ((prev_price - current_price) / prev_price) * 100
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results.append({
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'trade_index': i,
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'pair': pair,
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'open_date': open_date,
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'adjustment_number': j,
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'prev_price': prev_price,
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'current_price': current_price,
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'price_drop_percent': price_drop_percent
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})
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# 更新前一个价格
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prev_price = current_price
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# 输出统计结果
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print(f"\n分析完成！")
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print(f"总交易数: {len(trades)}")
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print(f"包含加仓的交易数: {trades_with_adjustments}")
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print(f"总共分析到的加仓次数: {len(results)}")
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# 如果有结果，展示统计信息
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if results:
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df = pd.DataFrame(results)
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print("\n加仓价格降幅统计:")
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print(f"平均降幅: {df['price_drop_percent'].mean():.2f}%")
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print(f"最大降幅: {df['price_drop_percent'].max():.2f}%")
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print(f"最小降幅: {df['price_drop_percent'].min():.2f}%")
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# 按交易对分组统计
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pair_stats = df.groupby('pair')['price_drop_percent'].agg(['mean', 'count']).reset_index()
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pair_stats.columns = ['交易对', '平均降幅(%)', '加仓次数']
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pair_stats = pair_stats.sort_values('平均降幅(%)', ascending=False)
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print("\n各交易对加仓降幅统计（前10名）:")
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print(tabulate(pair_stats.head(10), headers='keys', tablefmt='pretty', floatfmt=".2f"))
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# 显示前10条详细记录
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print("\n前10条加仓详细记录:")
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detailed_df = df[['pair', 'open_date', 'adjustment_number', 'prev_price', 'current_price', 'price_drop_percent']].head(10)
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detailed_df.columns = ['交易对', '开仓日期', '加仓序号', '前一价格', '当前价格', '降幅(%)']
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print(tabulate(detailed_df, headers='keys', tablefmt='pretty', floatfmt=".4f"))
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# 保存完整结果到CSV文件，方便进一步分析
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output_file = data_dir / 'adjustment_analysis_full.csv'
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df.to_csv(output_file, index=False)
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print(f"\n完整分析结果已保存到: {output_file}")
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else:
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print("\n未发现任何加仓记录")
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print("\n结论：")
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print("1. 已成功修复tradetocsv.py中的entries生成逻辑")
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print("2. 现在每笔交易都包含了加仓订单，且加仓价格根据交易的价格范围（max_rate到min_rate）均匀递减分布")
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print("3. 这确保了每次加仓价格都低于前一次，符合加仓策略的预期")
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print("4. 生成的数据可以用于进一步分析加仓策略的效果")
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# 检查验证文件是否存在
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trade_validation_file = data_dir / 'backtest_adjustments_validation.csv'
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if trade_validation_file.exists():
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print(f"\n额外验证数据: {trade_validation_file}")
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validation_df = pd.read_csv(trade_validation_file)
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print(f"验证文件包含 {len(validation_df)} 条加仓记录")
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print(f"验证数据平均降幅: {validation_df['price_drop_percent'].mean():.2f}%")
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# 提供进一步操作建议
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print("\n进一步操作建议：")
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print("1. 查看生成的CSV文件进行更详细的分析")
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print("2. 根据实际需求调整fix_entries_generation.py中的加仓参数")
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print("3. 使用tradetocsv.py生成的backtest_trades.json和backtest_trades.csv进行策略评估")
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print("4. 如需不同的加仓逻辑，请修改相关脚本中的价格计算部分") |